【Python机器学习】实验12 基于神经网络的回归-分类实验

Want5952023-08-17 16:41

文章目录

  • 神经网络的回归
    • [例1 基于神经网络的回归(简单例子)](#例1 基于神经网络的回归(简单例子))
      • [1.1 导入包](#1.1 导入包)
      • [1.2 构造数据集（随机构造的）](#1.2 构造数据集（随机构造的）)
      • [1.3 构造训练集和测试集](#1.3 构造训练集和测试集)
      • [1.4 构建神经网络模型](#1.4 构建神经网络模型)
      • [1.5 采用训练数据来训练神经网络模型](#1.5 采用训练数据来训练神经网络模型)
    • [实验1 基于神经网络的分类(鸢尾花数据集)](#实验1 基于神经网络的分类(鸢尾花数据集))
      • [1.1 导入包](#1.1 导入包)
      • [1.2 构造数据集](#1.2 构造数据集)
      • [1.3 构造训练集和测试集](#1.3 构造训练集和测试集)
      • [1.4 构建神经网络模型](#1.4 构建神经网络模型)
      • [1.5 采用训练数据来训练神经网络模型](#1.5 采用训练数据来训练神经网络模型)

神经网络的回归

例1 基于神经网络的回归(简单例子)

1.1 导入包

python 复制代码 
import torch
python 复制代码 
import numpy as np
from torch import nn
from sklearn.model_selection import train_test_split

1.2 构造数据集（随机构造的）

python 复制代码 
from torch.autograd import Variable
batch_n=100
hidden_layer=100
input_data=1000
output_data=10
python 复制代码 
x=Variable(torch.randn(batch_n,input_data),requires_grad=True)
y=Variable(torch.randn(batch_n,output_data),requires_grad=True)

1.3 构造训练集和测试集

python 复制代码 
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)
python 复制代码 
x_train.shape,x_test.shape,y_train.shape,y_test.shape
(torch.Size([80, 1000]),
 torch.Size([20, 1000]),
 torch.Size([80, 10]),
 torch.Size([20, 10]))
python 复制代码 
torch.Tensor(np.array([1,2]))
tensor([1., 2.])
python 复制代码 
y_test
tensor([[-0.1810,  0.2906,  0.4490,  1.3190, -1.1832, -0.0035,  0.5440, -0.8954,
          0.7686,  1.3758],
        [ 1.1767, -0.6170, -0.7946, -1.2191,  0.5998, -0.8591, -2.7796, -0.7918,
         -0.1282,  0.2730],
        [ 1.8079,  0.9862, -1.7850, -0.4031,  1.5472,  0.1663, -0.5043,  1.2402,
         -2.2270,  1.9437],
        [-0.0478,  0.1177, -0.4014,  0.6531, -2.0040,  1.5664,  2.0697, -0.5635,
         -0.4687,  1.5910],
        [ 1.5076,  1.0444, -1.7943,  0.7268,  1.1636,  0.1772, -1.0183, -1.0916,
          0.5012,  2.0798],
        [ 0.7027, -0.0999, -0.0670, -0.1838,  0.6959,  1.5484,  0.1950, -0.5757,
          1.4192, -0.6865],
        [ 1.7699, -1.9956,  0.1742, -0.6788, -2.0619,  0.8384,  2.1277, -1.2390,
         -1.0382,  0.5834],
        [ 0.8416,  1.6485, -0.0215,  0.0048, -1.7932,  0.1007, -2.4015,  0.3087,
         -0.7603,  0.9714],
        [-0.6723, -1.3535, -0.8598, -0.4294, -1.6416,  0.3986, -0.3160,  0.9952,
          0.6939, -1.2953],
        [ 0.1403,  0.2171, -1.0277, -0.6372,  0.2468,  1.6663,  0.3363,  0.5068,
         -0.0259, -0.8080],
        [ 0.9330,  0.8476, -0.3819,  0.8394,  1.1713, -0.6932, -0.0453, -1.3850,
          0.6089, -0.7219],
        [-0.1061, -2.8115, -1.7533, -0.3561,  0.5066,  0.5846,  0.2225,  0.7907,
          0.6693,  0.1164],
        [ 1.4511, -0.7063, -0.2785,  1.1644, -0.4726, -0.9858,  0.1105,  2.6274,
          0.8037,  0.1488],
        [ 0.9054, -0.1386,  0.6521, -2.7186, -1.1272, -0.7584, -1.1367, -0.0416,
         -0.0663,  0.6517],
        [-0.9568, -0.0174, -0.8611,  0.5748, -0.9300,  1.1043, -1.6796,  0.9629,
         -1.1011,  0.6005],
        [ 0.9963,  0.5226,  0.5209,  1.0107,  0.6931,  1.6149, -0.3450,  0.5082,
          1.2774, -0.1767],
        [ 0.3884, -1.8515, -0.6365, -0.1225,  1.2765, -0.1700,  0.4384,  0.0291,
          0.4540,  0.7085],
        [ 0.9688,  1.4026,  1.1516, -0.1575,  0.6101, -0.5406,  1.9612,  0.1654,
         -0.8425, -0.0459],
        [-1.5699,  0.0486, -1.7415,  1.5327,  0.0225, -1.1386, -0.6188,  0.3958,
          0.5564, -1.1593],
        [ 0.5734,  0.8675,  0.0328, -0.2371, -0.5879,  0.7541,  0.5935,  0.9097,
          0.9884,  0.6365]], grad_fn=<IndexBackward0>)

1.4 构建神经网络模型

python 复制代码 
class Nerual_Network(nn.Module):
    def __init__(self):
        super().__init__()
        self.hidden1=nn.Linear(input_data,hidden_layer)
        self.output=nn.Linear(hidden_layer,output_data)
        self.relu=nn.ReLU()
        self.softmax=nn.Softmax(dim=1)
    def forward(self,x):
        x=self.hidden1(x)
        x=self.relu(x)
        x=self.output(x)
        x=self.softmax(x)
        return x
python 复制代码 
import torch.optim as optim
model=Nerual_Network()
model
Nerual_Network(
  (hidden1): Linear(in_features=1000, out_features=100, bias=True)
  (output): Linear(in_features=100, out_features=10, bias=True)
  (relu): ReLU()
  (softmax): Softmax(dim=1)
)

1.5 采用训练数据来训练神经网络模型

python 复制代码 
epochs=1000
learnng_rate=0.003
critier=nn.MSELoss()
optimizer=optim.Adam(model.parameters(),lr=learnng_rate)
python 复制代码 
for i in range(epochs):
    outputs=model(x_train)
    loss=critier(outputs,y_train)
    print("Epoch:{},Loss:{:4f}".format(i,loss))
    optimizer.zero_grad()
    loss.backward(retain_graph=True)
    optimizer.step()
Epoch:0,Loss:0.948208
Epoch:1,Loss:0.896322
Epoch:2,Loss:0.855293
Epoch:3,Loss:0.819206
Epoch:4,Loss:0.790216
Epoch:5,Loss:0.769548
Epoch:6,Loss:0.755935
Epoch:7,Loss:0.747829
Epoch:8,Loss:0.743429
Epoch:9,Loss:0.741071
Epoch:10,Loss:0.739489
Epoch:11,Loss:0.738407
Epoch:12,Loss:0.737566
Epoch:13,Loss:0.736756
Epoch:14,Loss:0.736009
Epoch:15,Loss:0.735342
Epoch:16,Loss:0.734747
Epoch:17,Loss:0.734446
Epoch:18,Loss:0.734121
Epoch:19,Loss:0.733825
Epoch:20,Loss:0.733538
Epoch:21,Loss:0.733174
Epoch:22,Loss:0.732976
Epoch:23,Loss:0.732888
Epoch:24,Loss:0.732744
Epoch:25,Loss:0.732587
Epoch:26,Loss:0.732487
Epoch:27,Loss:0.732393
Epoch:28,Loss:0.732277
Epoch:29,Loss:0.732168
Epoch:30,Loss:0.732101
Epoch:31,Loss:0.732098
Epoch:32,Loss:0.731946
Epoch:33,Loss:0.731655
Epoch:34,Loss:0.731511
Epoch:35,Loss:0.731603
Epoch:36,Loss:0.731634
Epoch:37,Loss:0.731516
Epoch:38,Loss:0.731375
Epoch:39,Loss:0.731263
Epoch:40,Loss:0.731153
Epoch:41,Loss:0.731199
Epoch:42,Loss:0.731237
Epoch:43,Loss:0.731082
Epoch:44,Loss:0.730953
Epoch:45,Loss:0.730905
Epoch:46,Loss:0.730879
Epoch:47,Loss:0.730842
Epoch:48,Loss:0.730784
Epoch:49,Loss:0.730665
Epoch:50,Loss:0.730640
Epoch:51,Loss:0.730709
Epoch:52,Loss:0.730659
Epoch:53,Loss:0.730601
Epoch:54,Loss:0.730571
Epoch:55,Loss:0.730595
Epoch:56,Loss:0.730605
Epoch:57,Loss:0.730550
Epoch:58,Loss:0.730524
Epoch:59,Loss:0.730512
Epoch:60,Loss:0.730482
Epoch:61,Loss:0.730442
Epoch:62,Loss:0.730421
Epoch:63,Loss:0.730365
Epoch:64,Loss:0.730232
Epoch:65,Loss:0.730102
Epoch:66,Loss:0.730107
Epoch:67,Loss:0.730175
Epoch:68,Loss:0.730177
Epoch:69,Loss:0.730097
Epoch:70,Loss:0.730023
Epoch:71,Loss:0.730047
Epoch:72,Loss:0.730051
Epoch:73,Loss:0.729966
Epoch:74,Loss:0.729911
Epoch:75,Loss:0.729961
Epoch:76,Loss:0.729982
Epoch:77,Loss:0.729963
Epoch:78,Loss:0.729940
Epoch:79,Loss:0.729932
Epoch:80,Loss:0.729937
Epoch:81,Loss:0.729935
Epoch:82,Loss:0.729909
Epoch:83,Loss:0.729893
Epoch:84,Loss:0.729907
Epoch:85,Loss:0.729910
Epoch:86,Loss:0.729892
Epoch:87,Loss:0.729884
Epoch:88,Loss:0.729888
Epoch:89,Loss:0.729883
Epoch:90,Loss:0.729874
Epoch:91,Loss:0.729868
Epoch:92,Loss:0.729864
Epoch:93,Loss:0.729858
Epoch:94,Loss:0.729847
Epoch:95,Loss:0.729843
Epoch:96,Loss:0.729848
Epoch:97,Loss:0.729852
Epoch:98,Loss:0.729849
Epoch:99,Loss:0.729840
Epoch:100,Loss:0.729836
Epoch:101,Loss:0.729834
Epoch:102,Loss:0.729832
Epoch:103,Loss:0.729832
Epoch:104,Loss:0.729834
Epoch:105,Loss:0.729833
Epoch:106,Loss:0.729828
Epoch:107,Loss:0.729825
Epoch:108,Loss:0.729824
Epoch:109,Loss:0.729821
Epoch:110,Loss:0.729816
Epoch:111,Loss:0.729813
Epoch:112,Loss:0.729810
Epoch:113,Loss:0.729806
Epoch:114,Loss:0.729799
Epoch:115,Loss:0.729792
Epoch:116,Loss:0.729782
Epoch:117,Loss:0.729771
Epoch:118,Loss:0.729763
Epoch:119,Loss:0.729760
Epoch:120,Loss:0.729763
Epoch:121,Loss:0.729765
Epoch:122,Loss:0.729761
Epoch:123,Loss:0.729753
Epoch:124,Loss:0.729747
Epoch:125,Loss:0.729744
Epoch:126,Loss:0.729743
Epoch:127,Loss:0.729739
Epoch:128,Loss:0.729731
Epoch:129,Loss:0.729718
Epoch:130,Loss:0.729700
Epoch:131,Loss:0.729674
Epoch:132,Loss:0.729634
Epoch:133,Loss:0.729571
Epoch:134,Loss:0.729517
Epoch:135,Loss:0.729545
Epoch:136,Loss:0.729541
Epoch:137,Loss:0.729501
Epoch:138,Loss:0.729543
Epoch:139,Loss:0.729531
Epoch:140,Loss:0.729507
Epoch:141,Loss:0.729527
Epoch:142,Loss:0.729508
Epoch:143,Loss:0.729499
Epoch:144,Loss:0.729505
Epoch:145,Loss:0.729486
Epoch:146,Loss:0.729480
Epoch:147,Loss:0.729476
Epoch:148,Loss:0.729455
Epoch:149,Loss:0.729445
Epoch:150,Loss:0.729428
Epoch:151,Loss:0.729400
Epoch:152,Loss:0.729373
Epoch:153,Loss:0.729345
Epoch:154,Loss:0.729355
Epoch:155,Loss:0.729364
Epoch:156,Loss:0.729335
Epoch:157,Loss:0.729335
Epoch:158,Loss:0.729328
Epoch:159,Loss:0.729310
Epoch:160,Loss:0.729303
Epoch:161,Loss:0.729285
Epoch:162,Loss:0.729242
Epoch:163,Loss:0.729181
Epoch:164,Loss:0.729270
Epoch:165,Loss:0.729187
Epoch:166,Loss:0.729191
Epoch:167,Loss:0.729215
Epoch:168,Loss:0.729211
Epoch:169,Loss:0.729182
Epoch:170,Loss:0.729173
Epoch:171,Loss:0.729202
Epoch:172,Loss:0.729167
Epoch:173,Loss:0.729181
Epoch:174,Loss:0.729184
Epoch:175,Loss:0.729166
Epoch:176,Loss:0.729160
Epoch:177,Loss:0.729178
Epoch:178,Loss:0.729157
Epoch:179,Loss:0.729164
Epoch:180,Loss:0.729166
Epoch:181,Loss:0.729156
Epoch:182,Loss:0.729158
Epoch:183,Loss:0.729161
Epoch:184,Loss:0.729151
Epoch:185,Loss:0.729155
Epoch:186,Loss:0.729156
Epoch:187,Loss:0.729150
Epoch:188,Loss:0.729153
Epoch:189,Loss:0.729153
Epoch:190,Loss:0.729149
Epoch:191,Loss:0.729151
Epoch:192,Loss:0.729149
Epoch:193,Loss:0.729147
Epoch:194,Loss:0.729149
Epoch:195,Loss:0.729147
Epoch:196,Loss:0.729147
Epoch:197,Loss:0.729147
Epoch:198,Loss:0.729146
Epoch:199,Loss:0.729145
Epoch:200,Loss:0.729145
Epoch:201,Loss:0.729144
Epoch:202,Loss:0.729145
Epoch:203,Loss:0.729144
Epoch:204,Loss:0.729144
Epoch:205,Loss:0.729143
Epoch:206,Loss:0.729143
Epoch:207,Loss:0.729142
Epoch:208,Loss:0.729142
Epoch:209,Loss:0.729142
Epoch:210,Loss:0.729141
Epoch:211,Loss:0.729141
Epoch:212,Loss:0.729140
Epoch:213,Loss:0.729140
Epoch:214,Loss:0.729139
Epoch:215,Loss:0.729139
Epoch:216,Loss:0.729138
Epoch:217,Loss:0.729138
Epoch:218,Loss:0.729137
Epoch:219,Loss:0.729137
Epoch:220,Loss:0.729136
Epoch:221,Loss:0.729135
Epoch:222,Loss:0.729134
Epoch:223,Loss:0.729134
Epoch:224,Loss:0.729133
Epoch:225,Loss:0.729132
Epoch:226,Loss:0.729131
Epoch:227,Loss:0.729131
Epoch:228,Loss:0.729130
Epoch:229,Loss:0.729129
Epoch:230,Loss:0.729129
Epoch:231,Loss:0.729128
Epoch:232,Loss:0.729127
Epoch:233,Loss:0.729126
Epoch:234,Loss:0.729125
Epoch:235,Loss:0.729124
Epoch:236,Loss:0.729123
Epoch:237,Loss:0.729121
Epoch:238,Loss:0.729119
Epoch:239,Loss:0.729116
Epoch:240,Loss:0.729112
Epoch:241,Loss:0.729106
Epoch:242,Loss:0.729095
Epoch:243,Loss:0.729075
Epoch:244,Loss:0.729035
Epoch:245,Loss:0.728994
Epoch:246,Loss:0.729103
Epoch:247,Loss:0.729000
Epoch:248,Loss:0.729026
Epoch:249,Loss:0.729047
Epoch:250,Loss:0.729044
Epoch:251,Loss:0.729022
Epoch:252,Loss:0.728987
Epoch:253,Loss:0.729031
Epoch:254,Loss:0.728992
Epoch:255,Loss:0.728995
Epoch:256,Loss:0.729006
Epoch:257,Loss:0.728994
Epoch:258,Loss:0.728984
Epoch:259,Loss:0.728999
Epoch:260,Loss:0.728980
Epoch:261,Loss:0.728992
Epoch:262,Loss:0.728987
Epoch:263,Loss:0.728978
Epoch:264,Loss:0.728986
Epoch:265,Loss:0.728979
Epoch:266,Loss:0.728976
Epoch:267,Loss:0.728983
Epoch:268,Loss:0.728976
Epoch:269,Loss:0.728975
Epoch:270,Loss:0.728978
Epoch:271,Loss:0.728973
Epoch:272,Loss:0.728976
Epoch:273,Loss:0.728974
Epoch:274,Loss:0.728972
Epoch:275,Loss:0.728973
Epoch:276,Loss:0.728970
Epoch:277,Loss:0.728972
Epoch:278,Loss:0.728970
Epoch:279,Loss:0.728969
Epoch:280,Loss:0.728970
Epoch:281,Loss:0.728968
Epoch:282,Loss:0.728967
Epoch:283,Loss:0.728967
Epoch:284,Loss:0.728965
Epoch:285,Loss:0.728964
Epoch:286,Loss:0.728962
Epoch:287,Loss:0.728961
Epoch:288,Loss:0.728958
Epoch:289,Loss:0.728954
Epoch:290,Loss:0.728950
Epoch:291,Loss:0.728942
Epoch:292,Loss:0.728928
Epoch:293,Loss:0.728899
Epoch:294,Loss:0.728823
Epoch:295,Loss:0.728630
Epoch:296,Loss:0.728751
Epoch:297,Loss:0.728786
Epoch:298,Loss:0.728595
Epoch:299,Loss:0.728683
Epoch:300,Loss:0.728725
Epoch:301,Loss:0.728664
Epoch:302,Loss:0.728570
Epoch:303,Loss:0.728632
Epoch:304,Loss:0.728585
Epoch:305,Loss:0.728485
Epoch:306,Loss:0.728588
Epoch:307,Loss:0.728561
Epoch:308,Loss:0.728491
Epoch:309,Loss:0.728501
Epoch:310,Loss:0.728533
Epoch:311,Loss:0.728463
Epoch:312,Loss:0.728422
Epoch:313,Loss:0.728431
Epoch:314,Loss:0.728469
Epoch:315,Loss:0.728421
Epoch:316,Loss:0.728418
Epoch:317,Loss:0.728427
Epoch:318,Loss:0.728412
Epoch:319,Loss:0.728418
Epoch:320,Loss:0.728410
Epoch:321,Loss:0.728394
Epoch:322,Loss:0.728381
Epoch:323,Loss:0.728382
Epoch:324,Loss:0.728369
Epoch:325,Loss:0.728349
Epoch:326,Loss:0.728347
Epoch:327,Loss:0.728362
Epoch:328,Loss:0.728340
Epoch:329,Loss:0.728344
Epoch:330,Loss:0.728345
Epoch:331,Loss:0.728342
Epoch:332,Loss:0.728344
Epoch:333,Loss:0.728341
Epoch:334,Loss:0.728331
Epoch:335,Loss:0.728325
Epoch:336,Loss:0.728333
Epoch:337,Loss:0.728325
Epoch:338,Loss:0.728315
Epoch:339,Loss:0.728312
Epoch:340,Loss:0.728302
Epoch:341,Loss:0.728283
Epoch:342,Loss:0.728251
Epoch:343,Loss:0.728215
Epoch:344,Loss:0.728237
Epoch:345,Loss:0.728224
Epoch:346,Loss:0.728241
Epoch:347,Loss:0.728240
Epoch:348,Loss:0.728236
Epoch:349,Loss:0.728247
Epoch:350,Loss:0.728234
Epoch:351,Loss:0.728240
Epoch:352,Loss:0.728227
Epoch:353,Loss:0.728231
Epoch:354,Loss:0.728216
Epoch:355,Loss:0.728212
Epoch:356,Loss:0.728203
Epoch:357,Loss:0.728202
Epoch:358,Loss:0.728209
Epoch:359,Loss:0.728204
Epoch:360,Loss:0.728200
Epoch:361,Loss:0.728195
Epoch:362,Loss:0.728183
Epoch:363,Loss:0.728181
Epoch:364,Loss:0.728175
Epoch:365,Loss:0.728174
Epoch:366,Loss:0.728175
Epoch:367,Loss:0.728168
Epoch:368,Loss:0.728171
Epoch:369,Loss:0.728168
Epoch:370,Loss:0.728167
Epoch:371,Loss:0.728168
Epoch:372,Loss:0.728167
Epoch:373,Loss:0.728169
Epoch:374,Loss:0.728166
Epoch:375,Loss:0.728165
Epoch:376,Loss:0.728165
Epoch:377,Loss:0.728163
Epoch:378,Loss:0.728163
Epoch:379,Loss:0.728162
Epoch:380,Loss:0.728160
Epoch:381,Loss:0.728159
Epoch:382,Loss:0.728158
Epoch:383,Loss:0.728158
Epoch:384,Loss:0.728158
Epoch:385,Loss:0.728159
Epoch:386,Loss:0.728159
Epoch:387,Loss:0.728158
Epoch:388,Loss:0.728157
Epoch:389,Loss:0.728156
Epoch:390,Loss:0.728156
Epoch:391,Loss:0.728156
Epoch:392,Loss:0.728156
Epoch:393,Loss:0.728156
Epoch:394,Loss:0.728156
Epoch:395,Loss:0.728155
Epoch:396,Loss:0.728155
Epoch:397,Loss:0.728154
Epoch:398,Loss:0.728154
Epoch:399,Loss:0.728153
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Epoch:994,Loss:0.727955
Epoch:995,Loss:0.727956
Epoch:996,Loss:0.727957
Epoch:997,Loss:0.727957
Epoch:998,Loss:0.727958
Epoch:999,Loss:0.727956
python 复制代码 
loss=critier(model(x_test),y_test)
loss
tensor(1.0953, grad_fn=<MseLossBackward0>)

实验1 基于神经网络的分类(鸢尾花数据集)

1 数据用鸢尾花数据集（所有样本的四个特征，三个类别）

2 输出标签（one hot vector）

3 构建模型时输出端映射到0,1之间

4 修改损失函数为交叉熵函数

1.1 导入包

python 复制代码 
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from sklearn.datasets import load_iris
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split

1.2 构造数据集

python 复制代码 
iris=load_iris()
X,y=iris.data,iris.target
python 复制代码 
one_hot_vector=OneHotEncoder(sparse=False)
y=one_hot_vector.fit_transform(y.reshape(-1,1))

1.3 构造训练集和测试集

python 复制代码 
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)
python 复制代码 
X_train = torch.Tensor(X_train)
X_test = torch.Tensor(X_test)
y_train = torch.Tensor(y_train)
y_test = torch.Tensor(y_test)
python 复制代码 
X_train.shape,X_test.shape,y_train.shape,y_test.shape
(torch.Size([120, 4]),
 torch.Size([30, 4]),
 torch.Size([120, 3]),
 torch.Size([30, 3]))

1.4 构建神经网络模型

python 复制代码 
class Nerual_Network(nn.Module):
    def __init__(self):
        super().__init__()
        self.output=nn.Linear(X_train.shape[1],y_train.shape[1])
        self.sigmoid=nn.Sigmoid()
        self.softmax=nn.Softmax(dim=1)
    def forward(self,x):
        x=self.output(x)
        x=self.softmax(x)
        x=self.sigmoid(x)
        return x
python 复制代码 
model=Nerual_Network()
python 复制代码 
model
Nerual_Network(
  (output): Linear(in_features=4, out_features=3, bias=True)
  (sigmoid): Sigmoid()
  (softmax): Softmax(dim=1)
)

1.5 采用训练数据来训练神经网络模型

python 复制代码 
epochs=1000
learnng_rate=0.003
critier=nn.BCELoss()
optimizer=optim.Adam(model.parameters(),lr=learnng_rate)
python 复制代码 
for i in range(epochs):
    outputs=model(X_train)
    loss=critier(outputs,y_train)
    print("Epoch:{},Loss:{:4f}".format(i,loss))
    optimizer.zero_grad()
    loss.backward(retain_graph=True)
    optimizer.step()
Epoch:0,Loss:0.788641
Epoch:1,Loss:0.787205
Epoch:2,Loss:0.785736
Epoch:3,Loss:0.784244
Epoch:4,Loss:0.782739
Epoch:5,Loss:0.781233
Epoch:6,Loss:0.779737
Epoch:7,Loss:0.778262
Epoch:8,Loss:0.776822
Epoch:9,Loss:0.775428
Epoch:10,Loss:0.774091
Epoch:11,Loss:0.772821
Epoch:12,Loss:0.771625
Epoch:13,Loss:0.770507
Epoch:14,Loss:0.769467
Epoch:15,Loss:0.768504
Epoch:16,Loss:0.767612
Epoch:17,Loss:0.766784
Epoch:18,Loss:0.766013
Epoch:19,Loss:0.765293
Epoch:20,Loss:0.764617
Epoch:21,Loss:0.763979
Epoch:22,Loss:0.763374
Epoch:23,Loss:0.762798
Epoch:24,Loss:0.762245
Epoch:25,Loss:0.761712
Epoch:26,Loss:0.761195
Epoch:27,Loss:0.760692
Epoch:28,Loss:0.760198
Epoch:29,Loss:0.759711
Epoch:30,Loss:0.759228
Epoch:31,Loss:0.758748
Epoch:32,Loss:0.758268
Epoch:33,Loss:0.757787
Epoch:34,Loss:0.757302
Epoch:35,Loss:0.756813
Epoch:36,Loss:0.756318
Epoch:37,Loss:0.755816
Epoch:38,Loss:0.755305
Epoch:39,Loss:0.754786
Epoch:40,Loss:0.754257
Epoch:41,Loss:0.753716
Epoch:42,Loss:0.753165
Epoch:43,Loss:0.752602
Epoch:44,Loss:0.752026
Epoch:45,Loss:0.751437
Epoch:46,Loss:0.750836
Epoch:47,Loss:0.750221
Epoch:48,Loss:0.749594
Epoch:49,Loss:0.748953
Epoch:50,Loss:0.748300
Epoch:51,Loss:0.747635
Epoch:52,Loss:0.746958
Epoch:53,Loss:0.746270
Epoch:54,Loss:0.745572
Epoch:55,Loss:0.744864
Epoch:56,Loss:0.744148
Epoch:57,Loss:0.743425
Epoch:58,Loss:0.742694
Epoch:59,Loss:0.741958
Epoch:60,Loss:0.741217
Epoch:61,Loss:0.740473
Epoch:62,Loss:0.739725
Epoch:63,Loss:0.738975
Epoch:64,Loss:0.738224
Epoch:65,Loss:0.737471
Epoch:66,Loss:0.736718
Epoch:67,Loss:0.735964
Epoch:68,Loss:0.735211
Epoch:69,Loss:0.734458
Epoch:70,Loss:0.733706
Epoch:71,Loss:0.732954
Epoch:72,Loss:0.732204
Epoch:73,Loss:0.731456
Epoch:74,Loss:0.730709
Epoch:75,Loss:0.729964
Epoch:76,Loss:0.729223
Epoch:77,Loss:0.728484
Epoch:78,Loss:0.727750
Epoch:79,Loss:0.727019
Epoch:80,Loss:0.726294
Epoch:81,Loss:0.725574
Epoch:82,Loss:0.724860
Epoch:83,Loss:0.724152
Epoch:84,Loss:0.723452
Epoch:85,Loss:0.722758
Epoch:86,Loss:0.722072
Epoch:87,Loss:0.721393
Epoch:88,Loss:0.720722
Epoch:89,Loss:0.720058
Epoch:90,Loss:0.719403
Epoch:91,Loss:0.718755
Epoch:92,Loss:0.718115
Epoch:93,Loss:0.717483
Epoch:94,Loss:0.716859
Epoch:95,Loss:0.716242
Epoch:96,Loss:0.715634
Epoch:97,Loss:0.715033
Epoch:98,Loss:0.714440
Epoch:99,Loss:0.713856
Epoch:100,Loss:0.713278
Epoch:101,Loss:0.712709
Epoch:102,Loss:0.712148
Epoch:103,Loss:0.711594
Epoch:104,Loss:0.711047
Epoch:105,Loss:0.710508
Epoch:106,Loss:0.709977
Epoch:107,Loss:0.709452
Epoch:108,Loss:0.708935
Epoch:109,Loss:0.708425
Epoch:110,Loss:0.707922
Epoch:111,Loss:0.707425
Epoch:112,Loss:0.706936
Epoch:113,Loss:0.706452
Epoch:114,Loss:0.705976
Epoch:115,Loss:0.705505
Epoch:116,Loss:0.705041
Epoch:117,Loss:0.704583
Epoch:118,Loss:0.704132
Epoch:119,Loss:0.703686
Epoch:120,Loss:0.703246
Epoch:121,Loss:0.702812
Epoch:122,Loss:0.702383
Epoch:123,Loss:0.701960
Epoch:124,Loss:0.701543
Epoch:125,Loss:0.701130
Epoch:126,Loss:0.700724
Epoch:127,Loss:0.700322
Epoch:128,Loss:0.699925
Epoch:129,Loss:0.699534
Epoch:130,Loss:0.699147
Epoch:131,Loss:0.698766
Epoch:132,Loss:0.698389
Epoch:133,Loss:0.698016
Epoch:134,Loss:0.697648
Epoch:135,Loss:0.697285
Epoch:136,Loss:0.696926
Epoch:137,Loss:0.696571
Epoch:138,Loss:0.696221
Epoch:139,Loss:0.695875
Epoch:140,Loss:0.695533
Epoch:141,Loss:0.695194
Epoch:142,Loss:0.694860
Epoch:143,Loss:0.694529
Epoch:144,Loss:0.694202
Epoch:145,Loss:0.693879
Epoch:146,Loss:0.693560
Epoch:147,Loss:0.693243
Epoch:148,Loss:0.692931
Epoch:149,Loss:0.692621
Epoch:150,Loss:0.692315
Epoch:151,Loss:0.692012
Epoch:152,Loss:0.691712
Epoch:153,Loss:0.691416
Epoch:154,Loss:0.691122
Epoch:155,Loss:0.690832
Epoch:156,Loss:0.690544
Epoch:157,Loss:0.690259
Epoch:158,Loss:0.689977
Epoch:159,Loss:0.689698
Epoch:160,Loss:0.689421
Epoch:161,Loss:0.689147
Epoch:162,Loss:0.688875
Epoch:163,Loss:0.688606
Epoch:164,Loss:0.688340
Epoch:165,Loss:0.688076
Epoch:166,Loss:0.687814
Epoch:167,Loss:0.687554
Epoch:168,Loss:0.687297
Epoch:169,Loss:0.687042
Epoch:170,Loss:0.686789
Epoch:171,Loss:0.686539
Epoch:172,Loss:0.686290
Epoch:173,Loss:0.686044
Epoch:174,Loss:0.685799
Epoch:175,Loss:0.685557
Epoch:176,Loss:0.685316
Epoch:177,Loss:0.685077
Epoch:178,Loss:0.684840
Epoch:179,Loss:0.684605
Epoch:180,Loss:0.684372
Epoch:181,Loss:0.684141
Epoch:182,Loss:0.683911
Epoch:183,Loss:0.683683
Epoch:184,Loss:0.683456
Epoch:185,Loss:0.683231
Epoch:186,Loss:0.683008
Epoch:187,Loss:0.682786
Epoch:188,Loss:0.682566
Epoch:189,Loss:0.682347
Epoch:190,Loss:0.682130
Epoch:191,Loss:0.681914
Epoch:192,Loss:0.681700
Epoch:193,Loss:0.681486
Epoch:194,Loss:0.681275
Epoch:195,Loss:0.681064
Epoch:196,Loss:0.680855
Epoch:197,Loss:0.680647
Epoch:198,Loss:0.680441
Epoch:199,Loss:0.680236
Epoch:200,Loss:0.680032
Epoch:201,Loss:0.679829
Epoch:202,Loss:0.679627
Epoch:203,Loss:0.679426
Epoch:204,Loss:0.679227
Epoch:205,Loss:0.679029
Epoch:206,Loss:0.678831
Epoch:207,Loss:0.678635
Epoch:208,Loss:0.678440
Epoch:209,Loss:0.678246
Epoch:210,Loss:0.678053
Epoch:211,Loss:0.677861
Epoch:212,Loss:0.677670
Epoch:213,Loss:0.677480
Epoch:214,Loss:0.677291
Epoch:215,Loss:0.677102
Epoch:216,Loss:0.676915
Epoch:217,Loss:0.676729
Epoch:218,Loss:0.676543
Epoch:219,Loss:0.676359
Epoch:220,Loss:0.676175
Epoch:221,Loss:0.675992
Epoch:222,Loss:0.675810
Epoch:223,Loss:0.675628
Epoch:224,Loss:0.675448
Epoch:225,Loss:0.675268
Epoch:226,Loss:0.675089
Epoch:227,Loss:0.674911
Epoch:228,Loss:0.674734
Epoch:229,Loss:0.674557
Epoch:230,Loss:0.674381
Epoch:231,Loss:0.674206
Epoch:232,Loss:0.674032
Epoch:233,Loss:0.673858
Epoch:234,Loss:0.673685
Epoch:235,Loss:0.673513
Epoch:236,Loss:0.673341
Epoch:237,Loss:0.673170
Epoch:238,Loss:0.673000
Epoch:239,Loss:0.672830
Epoch:240,Loss:0.672661
Epoch:241,Loss:0.672493
Epoch:242,Loss:0.672325
Epoch:243,Loss:0.672158
Epoch:244,Loss:0.671991
Epoch:245,Loss:0.671825
Epoch:246,Loss:0.671660
Epoch:247,Loss:0.671495
Epoch:248,Loss:0.671331
Epoch:249,Loss:0.671167
Epoch:250,Loss:0.671004
Epoch:251,Loss:0.670842
Epoch:252,Loss:0.670680
Epoch:253,Loss:0.670518
Epoch:254,Loss:0.670357
Epoch:255,Loss:0.670197
Epoch:256,Loss:0.670037
Epoch:257,Loss:0.669878
Epoch:258,Loss:0.669719
Epoch:259,Loss:0.669561
Epoch:260,Loss:0.669403
Epoch:261,Loss:0.669246
Epoch:262,Loss:0.669089
Epoch:263,Loss:0.668932
Epoch:264,Loss:0.668777
Epoch:265,Loss:0.668621
Epoch:266,Loss:0.668466
Epoch:267,Loss:0.668312
Epoch:268,Loss:0.668158
Epoch:269,Loss:0.668004
Epoch:270,Loss:0.667851
Epoch:271,Loss:0.667699
Epoch:272,Loss:0.667547
Epoch:273,Loss:0.667395
Epoch:274,Loss:0.667244
Epoch:275,Loss:0.667093
Epoch:276,Loss:0.666942
Epoch:277,Loss:0.666792
Epoch:278,Loss:0.666643
Epoch:279,Loss:0.666493
Epoch:280,Loss:0.666345
Epoch:281,Loss:0.666196
Epoch:282,Loss:0.666048
Epoch:283,Loss:0.665901
Epoch:284,Loss:0.665754
Epoch:285,Loss:0.665607
Epoch:286,Loss:0.665460
Epoch:287,Loss:0.665314
Epoch:288,Loss:0.665169
Epoch:289,Loss:0.665023
Epoch:290,Loss:0.664879
Epoch:291,Loss:0.664734
Epoch:292,Loss:0.664590
Epoch:293,Loss:0.664446
Epoch:294,Loss:0.664303
Epoch:295,Loss:0.664160
Epoch:296,Loss:0.664017
Epoch:297,Loss:0.663875
Epoch:298,Loss:0.663733
Epoch:299,Loss:0.663591
Epoch:300,Loss:0.663450
Epoch:301,Loss:0.663309
Epoch:302,Loss:0.663169
Epoch:303,Loss:0.663028
Epoch:304,Loss:0.662889
Epoch:305,Loss:0.662749
Epoch:306,Loss:0.662610
Epoch:307,Loss:0.662471
Epoch:308,Loss:0.662332
Epoch:309,Loss:0.662194
Epoch:310,Loss:0.662056
Epoch:311,Loss:0.661919
Epoch:312,Loss:0.661781
Epoch:313,Loss:0.661644
Epoch:314,Loss:0.661508
Epoch:315,Loss:0.661372
Epoch:316,Loss:0.661236
Epoch:317,Loss:0.661100
Epoch:318,Loss:0.660964
Epoch:319,Loss:0.660829
Epoch:320,Loss:0.660695
Epoch:321,Loss:0.660560
Epoch:322,Loss:0.660426
Epoch:323,Loss:0.660292
Epoch:324,Loss:0.660159
Epoch:325,Loss:0.660026
Epoch:326,Loss:0.659893
Epoch:327,Loss:0.659760
Epoch:328,Loss:0.659628
Epoch:329,Loss:0.659496
Epoch:330,Loss:0.659364
Epoch:331,Loss:0.659232
Epoch:332,Loss:0.659101
Epoch:333,Loss:0.658970
Epoch:334,Loss:0.658840
Epoch:335,Loss:0.658709
Epoch:336,Loss:0.658579
Epoch:337,Loss:0.658450
Epoch:338,Loss:0.658320
Epoch:339,Loss:0.658191
Epoch:340,Loss:0.658062
Epoch:341,Loss:0.657933
Epoch:342,Loss:0.657805
Epoch:343,Loss:0.657677
Epoch:344,Loss:0.657549
Epoch:345,Loss:0.657421
Epoch:346,Loss:0.657294
Epoch:347,Loss:0.657167
Epoch:348,Loss:0.657040
Epoch:349,Loss:0.656914
Epoch:350,Loss:0.656788
Epoch:351,Loss:0.656662
Epoch:352,Loss:0.656536
Epoch:353,Loss:0.656411
Epoch:354,Loss:0.656285
Epoch:355,Loss:0.656161
Epoch:356,Loss:0.656036
Epoch:357,Loss:0.655911
Epoch:358,Loss:0.655787
Epoch:359,Loss:0.655663
Epoch:360,Loss:0.655540
Epoch:361,Loss:0.655416
Epoch:362,Loss:0.655293
Epoch:363,Loss:0.655171
Epoch:364,Loss:0.655048
Epoch:365,Loss:0.654925
Epoch:366,Loss:0.654803
Epoch:367,Loss:0.654682
Epoch:368,Loss:0.654560
Epoch:369,Loss:0.654438
Epoch:370,Loss:0.654317
Epoch:371,Loss:0.654196
Epoch:372,Loss:0.654076
Epoch:373,Loss:0.653955
Epoch:374,Loss:0.653835
Epoch:375,Loss:0.653715
Epoch:376,Loss:0.653596
Epoch:377,Loss:0.653476
Epoch:378,Loss:0.653357
Epoch:379,Loss:0.653238
Epoch:380,Loss:0.653119
Epoch:381,Loss:0.653001
Epoch:382,Loss:0.652883
Epoch:383,Loss:0.652765
Epoch:384,Loss:0.652647
Epoch:385,Loss:0.652529
Epoch:386,Loss:0.652412
Epoch:387,Loss:0.652295
Epoch:388,Loss:0.652178
Epoch:389,Loss:0.652062
Epoch:390,Loss:0.651945
Epoch:391,Loss:0.651829
Epoch:392,Loss:0.651713
Epoch:393,Loss:0.651597
Epoch:394,Loss:0.651482
Epoch:395,Loss:0.651367
Epoch:396,Loss:0.651252
Epoch:397,Loss:0.651137
Epoch:398,Loss:0.651022
Epoch:399,Loss:0.650908
Epoch:400,Loss:0.650794
Epoch:401,Loss:0.650680
Epoch:402,Loss:0.650566
Epoch:403,Loss:0.650453
Epoch:404,Loss:0.650340
Epoch:405,Loss:0.650227
Epoch:406,Loss:0.650114
Epoch:407,Loss:0.650001
Epoch:408,Loss:0.649889
Epoch:409,Loss:0.649777
Epoch:410,Loss:0.649665
Epoch:411,Loss:0.649553
Epoch:412,Loss:0.649442
Epoch:413,Loss:0.649331
Epoch:414,Loss:0.649220
Epoch:415,Loss:0.649109
Epoch:416,Loss:0.648998
Epoch:417,Loss:0.648888
Epoch:418,Loss:0.648778
Epoch:419,Loss:0.648668
Epoch:420,Loss:0.648558
Epoch:421,Loss:0.648448
Epoch:422,Loss:0.648339
Epoch:423,Loss:0.648230
Epoch:424,Loss:0.648121
Epoch:425,Loss:0.648013
Epoch:426,Loss:0.647904
Epoch:427,Loss:0.647796
Epoch:428,Loss:0.647688
Epoch:429,Loss:0.647580
Epoch:430,Loss:0.647472
Epoch:431,Loss:0.647365
Epoch:432,Loss:0.647258
Epoch:433,Loss:0.647151
Epoch:434,Loss:0.647044
Epoch:435,Loss:0.646937
Epoch:436,Loss:0.646831
Epoch:437,Loss:0.646725
Epoch:438,Loss:0.646619
Epoch:439,Loss:0.646513
Epoch:440,Loss:0.646407
Epoch:441,Loss:0.646302
Epoch:442,Loss:0.646197
Epoch:443,Loss:0.646092
Epoch:444,Loss:0.645987
Epoch:445,Loss:0.645882
Epoch:446,Loss:0.645778
Epoch:447,Loss:0.645674
Epoch:448,Loss:0.645570
Epoch:449,Loss:0.645466
Epoch:450,Loss:0.645362
Epoch:451,Loss:0.645259
Epoch:452,Loss:0.645156
Epoch:453,Loss:0.645053
Epoch:454,Loss:0.644950
Epoch:455,Loss:0.644848
Epoch:456,Loss:0.644745
Epoch:457,Loss:0.644643
Epoch:458,Loss:0.644541
Epoch:459,Loss:0.644439
Epoch:460,Loss:0.644338
Epoch:461,Loss:0.644236
Epoch:462,Loss:0.644135
Epoch:463,Loss:0.644034
Epoch:464,Loss:0.643933
Epoch:465,Loss:0.643833
Epoch:466,Loss:0.643732
Epoch:467,Loss:0.643632
Epoch:468,Loss:0.643532
Epoch:469,Loss:0.643432
Epoch:470,Loss:0.643332
Epoch:471,Loss:0.643233
Epoch:472,Loss:0.643133
Epoch:473,Loss:0.643034
Epoch:474,Loss:0.642935
Epoch:475,Loss:0.642837
Epoch:476,Loss:0.642738
Epoch:477,Loss:0.642640
Epoch:478,Loss:0.642542
Epoch:479,Loss:0.642444
Epoch:480,Loss:0.642346
Epoch:481,Loss:0.642248
Epoch:482,Loss:0.642151
Epoch:483,Loss:0.642054
Epoch:484,Loss:0.641956
Epoch:485,Loss:0.641860
Epoch:486,Loss:0.641763
Epoch:487,Loss:0.641666
Epoch:488,Loss:0.641570
Epoch:489,Loss:0.641474
Epoch:490,Loss:0.641378
Epoch:491,Loss:0.641282
Epoch:492,Loss:0.641187
Epoch:493,Loss:0.641091
Epoch:494,Loss:0.640996
Epoch:495,Loss:0.640901
Epoch:496,Loss:0.640806
Epoch:497,Loss:0.640712
Epoch:498,Loss:0.640617
Epoch:499,Loss:0.640523
Epoch:500,Loss:0.640429
Epoch:501,Loss:0.640335
Epoch:502,Loss:0.640241
Epoch:503,Loss:0.640147
Epoch:504,Loss:0.640054
Epoch:505,Loss:0.639961
Epoch:506,Loss:0.639867
Epoch:507,Loss:0.639775
Epoch:508,Loss:0.639682
Epoch:509,Loss:0.639589
Epoch:510,Loss:0.639497
Epoch:511,Loss:0.639405
Epoch:512,Loss:0.639313
Epoch:513,Loss:0.639221
Epoch:514,Loss:0.639129
Epoch:515,Loss:0.639038
Epoch:516,Loss:0.638947
Epoch:517,Loss:0.638855
Epoch:518,Loss:0.638764
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Epoch:648,Loss:0.628291
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Epoch:694,Loss:0.625155
Epoch:695,Loss:0.625090
Epoch:696,Loss:0.625025
Epoch:697,Loss:0.624960
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Epoch:699,Loss:0.624830
Epoch:700,Loss:0.624765
Epoch:701,Loss:0.624701
Epoch:702,Loss:0.624637
Epoch:703,Loss:0.624572
Epoch:704,Loss:0.624508
Epoch:705,Loss:0.624444
Epoch:706,Loss:0.624380
Epoch:707,Loss:0.624316
Epoch:708,Loss:0.624252
Epoch:709,Loss:0.624189
Epoch:710,Loss:0.624125
Epoch:711,Loss:0.624062
Epoch:712,Loss:0.623999
Epoch:713,Loss:0.623936
Epoch:714,Loss:0.623873
Epoch:715,Loss:0.623810
Epoch:716,Loss:0.623747
Epoch:717,Loss:0.623684
Epoch:718,Loss:0.623622
Epoch:719,Loss:0.623559
Epoch:720,Loss:0.623497
Epoch:721,Loss:0.623435
Epoch:722,Loss:0.623372
Epoch:723,Loss:0.623310
Epoch:724,Loss:0.623249
Epoch:725,Loss:0.623187
Epoch:726,Loss:0.623125
Epoch:727,Loss:0.623064
Epoch:728,Loss:0.623002
Epoch:729,Loss:0.622941
Epoch:730,Loss:0.622880
Epoch:731,Loss:0.622819
Epoch:732,Loss:0.622757
Epoch:733,Loss:0.622697
Epoch:734,Loss:0.622636
Epoch:735,Loss:0.622575
Epoch:736,Loss:0.622515
Epoch:737,Loss:0.622454
Epoch:738,Loss:0.622394
Epoch:739,Loss:0.622334
Epoch:740,Loss:0.622274
Epoch:741,Loss:0.622214
Epoch:742,Loss:0.622154
Epoch:743,Loss:0.622094
Epoch:744,Loss:0.622034
Epoch:745,Loss:0.621975
Epoch:746,Loss:0.621915
Epoch:747,Loss:0.621856
Epoch:748,Loss:0.621796
Epoch:749,Loss:0.621737
Epoch:750,Loss:0.621678
Epoch:751,Loss:0.621619
Epoch:752,Loss:0.621561
Epoch:753,Loss:0.621502
Epoch:754,Loss:0.621443
Epoch:755,Loss:0.621385
Epoch:756,Loss:0.621326
Epoch:757,Loss:0.621268
Epoch:758,Loss:0.621210
Epoch:759,Loss:0.621152
Epoch:760,Loss:0.621094
Epoch:761,Loss:0.621036
Epoch:762,Loss:0.620978
Epoch:763,Loss:0.620920
Epoch:764,Loss:0.620863
Epoch:765,Loss:0.620805
Epoch:766,Loss:0.620748
Epoch:767,Loss:0.620691
Epoch:768,Loss:0.620634
Epoch:769,Loss:0.620577
Epoch:770,Loss:0.620520
Epoch:771,Loss:0.620463
Epoch:772,Loss:0.620406
Epoch:773,Loss:0.620349
Epoch:774,Loss:0.620293
Epoch:775,Loss:0.620236
Epoch:776,Loss:0.620180
Epoch:777,Loss:0.620124
Epoch:778,Loss:0.620068
Epoch:779,Loss:0.620012
Epoch:780,Loss:0.619956
Epoch:781,Loss:0.619900
Epoch:782,Loss:0.619844
Epoch:783,Loss:0.619788
Epoch:784,Loss:0.619733
Epoch:785,Loss:0.619677
Epoch:786,Loss:0.619622
Epoch:787,Loss:0.619567
Epoch:788,Loss:0.619512
Epoch:789,Loss:0.619457
Epoch:790,Loss:0.619402
Epoch:791,Loss:0.619347
Epoch:792,Loss:0.619292
Epoch:793,Loss:0.619237
Epoch:794,Loss:0.619183
Epoch:795,Loss:0.619128
Epoch:796,Loss:0.619074
Epoch:797,Loss:0.619020
Epoch:798,Loss:0.618965
Epoch:799,Loss:0.618911
Epoch:800,Loss:0.618857
Epoch:801,Loss:0.618803
Epoch:802,Loss:0.618750
Epoch:803,Loss:0.618696
Epoch:804,Loss:0.618642
Epoch:805,Loss:0.618589
Epoch:806,Loss:0.618535
Epoch:807,Loss:0.618482
Epoch:808,Loss:0.618429
Epoch:809,Loss:0.618376
Epoch:810,Loss:0.618322
Epoch:811,Loss:0.618270
Epoch:812,Loss:0.618217
Epoch:813,Loss:0.618164
Epoch:814,Loss:0.618111
Epoch:815,Loss:0.618059
Epoch:816,Loss:0.618006
Epoch:817,Loss:0.617954
Epoch:818,Loss:0.617901
Epoch:819,Loss:0.617849
Epoch:820,Loss:0.617797
Epoch:821,Loss:0.617745
Epoch:822,Loss:0.617693
Epoch:823,Loss:0.617641
Epoch:824,Loss:0.617589
Epoch:825,Loss:0.617538
Epoch:826,Loss:0.617486
Epoch:827,Loss:0.617435
Epoch:828,Loss:0.617383
Epoch:829,Loss:0.617332
Epoch:830,Loss:0.617281
Epoch:831,Loss:0.617230
Epoch:832,Loss:0.617179
Epoch:833,Loss:0.617128
Epoch:834,Loss:0.617077
Epoch:835,Loss:0.617026
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Epoch:837,Loss:0.616925
Epoch:838,Loss:0.616874
Epoch:839,Loss:0.616824
Epoch:840,Loss:0.616773
Epoch:841,Loss:0.616723
Epoch:842,Loss:0.616673
Epoch:843,Loss:0.616623
Epoch:844,Loss:0.616573
Epoch:845,Loss:0.616523
Epoch:846,Loss:0.616473
Epoch:847,Loss:0.616423
Epoch:848,Loss:0.616374
Epoch:849,Loss:0.616324
Epoch:850,Loss:0.616275
Epoch:851,Loss:0.616225
Epoch:852,Loss:0.616176
Epoch:853,Loss:0.616127
Epoch:854,Loss:0.616078
Epoch:855,Loss:0.616029
Epoch:856,Loss:0.615980
Epoch:857,Loss:0.615931
Epoch:858,Loss:0.615882
Epoch:859,Loss:0.615833
Epoch:860,Loss:0.615785
Epoch:861,Loss:0.615736
Epoch:862,Loss:0.615688
Epoch:863,Loss:0.615639
Epoch:864,Loss:0.615591
Epoch:865,Loss:0.615543
Epoch:866,Loss:0.615495
Epoch:867,Loss:0.615447
Epoch:868,Loss:0.615399
Epoch:869,Loss:0.615351
Epoch:870,Loss:0.615303
Epoch:871,Loss:0.615255
Epoch:872,Loss:0.615208
Epoch:873,Loss:0.615160
Epoch:874,Loss:0.615113
Epoch:875,Loss:0.615065
Epoch:876,Loss:0.615018
Epoch:877,Loss:0.614971
Epoch:878,Loss:0.614923
Epoch:879,Loss:0.614876
Epoch:880,Loss:0.614829
Epoch:881,Loss:0.614783
Epoch:882,Loss:0.614736
Epoch:883,Loss:0.614689
Epoch:884,Loss:0.614642
Epoch:885,Loss:0.614596
Epoch:886,Loss:0.614549
Epoch:887,Loss:0.614503
Epoch:888,Loss:0.614456
Epoch:889,Loss:0.614410
Epoch:890,Loss:0.614364
Epoch:891,Loss:0.614318
Epoch:892,Loss:0.614272
Epoch:893,Loss:0.614226
Epoch:894,Loss:0.614180
Epoch:895,Loss:0.614134
Epoch:896,Loss:0.614088
Epoch:897,Loss:0.614043
Epoch:898,Loss:0.613997
Epoch:899,Loss:0.613952
Epoch:900,Loss:0.613906
Epoch:901,Loss:0.613861
Epoch:902,Loss:0.613816
Epoch:903,Loss:0.613770
Epoch:904,Loss:0.613725
Epoch:905,Loss:0.613680
Epoch:906,Loss:0.613635
Epoch:907,Loss:0.613590
Epoch:908,Loss:0.613545
Epoch:909,Loss:0.613501
Epoch:910,Loss:0.613456
Epoch:911,Loss:0.613411
Epoch:912,Loss:0.613367
Epoch:913,Loss:0.613323
Epoch:914,Loss:0.613278
Epoch:915,Loss:0.613234
Epoch:916,Loss:0.613190
Epoch:917,Loss:0.613145
Epoch:918,Loss:0.613101
Epoch:919,Loss:0.613057
Epoch:920,Loss:0.613014
Epoch:921,Loss:0.612970
Epoch:922,Loss:0.612926
Epoch:923,Loss:0.612882
Epoch:924,Loss:0.612839
Epoch:925,Loss:0.612795
Epoch:926,Loss:0.612752
Epoch:927,Loss:0.612708
Epoch:928,Loss:0.612665
Epoch:929,Loss:0.612621
Epoch:930,Loss:0.612578
Epoch:931,Loss:0.612535
Epoch:932,Loss:0.612492
Epoch:933,Loss:0.612449
Epoch:934,Loss:0.612406
Epoch:935,Loss:0.612363
Epoch:936,Loss:0.612321
Epoch:937,Loss:0.612278
Epoch:938,Loss:0.612235
Epoch:939,Loss:0.612193
Epoch:940,Loss:0.612150
Epoch:941,Loss:0.612108
Epoch:942,Loss:0.612066
Epoch:943,Loss:0.612023
Epoch:944,Loss:0.611981
Epoch:945,Loss:0.611939
Epoch:946,Loss:0.611897
Epoch:947,Loss:0.611855
Epoch:948,Loss:0.611813
Epoch:949,Loss:0.611771
Epoch:950,Loss:0.611729
Epoch:951,Loss:0.611688
Epoch:952,Loss:0.611646
Epoch:953,Loss:0.611604
Epoch:954,Loss:0.611563
Epoch:955,Loss:0.611521
Epoch:956,Loss:0.611480
Epoch:957,Loss:0.611439
Epoch:958,Loss:0.611398
Epoch:959,Loss:0.611356
Epoch:960,Loss:0.611315
Epoch:961,Loss:0.611274
Epoch:962,Loss:0.611233
Epoch:963,Loss:0.611192
Epoch:964,Loss:0.611151
Epoch:965,Loss:0.611111
Epoch:966,Loss:0.611070
Epoch:967,Loss:0.611029
Epoch:968,Loss:0.610989
Epoch:969,Loss:0.610948
Epoch:970,Loss:0.610908
Epoch:971,Loss:0.610868
Epoch:972,Loss:0.610827
Epoch:973,Loss:0.610787
Epoch:974,Loss:0.610747
Epoch:975,Loss:0.610707
Epoch:976,Loss:0.610667
Epoch:977,Loss:0.610627
Epoch:978,Loss:0.610587
Epoch:979,Loss:0.610547
Epoch:980,Loss:0.610507
Epoch:981,Loss:0.610467
Epoch:982,Loss:0.610428
Epoch:983,Loss:0.610388
Epoch:984,Loss:0.610349
Epoch:985,Loss:0.610309
Epoch:986,Loss:0.610270
Epoch:987,Loss:0.610231
Epoch:988,Loss:0.610191
Epoch:989,Loss:0.610152
Epoch:990,Loss:0.610113
Epoch:991,Loss:0.610074
Epoch:992,Loss:0.610035
Epoch:993,Loss:0.609996
Epoch:994,Loss:0.609957
Epoch:995,Loss:0.609918
Epoch:996,Loss:0.609879
Epoch:997,Loss:0.609841
Epoch:998,Loss:0.609802
Epoch:999,Loss:0.609763
python 复制代码 
loss=critier(model(X_test),y_test)
loss
tensor(0.6142, grad_fn=<BinaryCrossEntropyBackward0>)
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